test/test_ops.py

# Owner(s): ["module: unknown"]

from collections.abc import Sequence
from functools import partial
import warnings
import unittest
import itertools
import torch
import contextlib
from importlib import import_module

from torch.testing import make_tensor
from torch.testing._internal.common_dtype import (
    floating_and_complex_types_and,
    all_types_and_complex_and,
)
from torch.testing._internal.common_utils import (
    TestCase,
    is_iterable_of_tensors,
    run_tests,
    IS_SANDCASTLE,
    clone_input_helper,
    IS_IN_CI,
    suppress_warnings,
    noncontiguous_like,
    TEST_WITH_ASAN,
    IS_WINDOWS,
    IS_FBCODE,
    first_sample,
    parametrize,
)
from torch.testing._internal.common_methods_invocations import (
    op_db,
    _NOTHING,
    UnaryUfuncInfo,
    ReductionOpInfo,
    SpectralFuncInfo,
    ops_and_refs,
    python_ref_db,
    BinaryUfuncInfo,
)
from torch.testing._internal.common_device_type import (
    deviceCountAtLeast,
    instantiate_device_type_tests,
    ops,
    onlyCUDA,
    onlyNativeDeviceTypes,
    OpDTypes,
    skipMeta,
)
from torch.utils._pytree import tree_map
import torch._prims as prims
from torch._prims.context import TorchRefsMode

import torch.testing._internal.opinfo_helper as opinfo_helper
from torch.testing._internal import composite_compliance

from torch.utils._pytree import tree_flatten

# TODO: fixme https://github.com/pytorch/pytorch/issues/68972
torch.set_default_dtype(torch.float32)

# variant testing is only done with torch.float and torch.cfloat to avoid
#   excessive test times and maximize signal to noise ratio
_variant_ops = partial(
    ops, dtypes=OpDTypes.supported, allowed_dtypes=(torch.float, torch.cfloat)
)

# Get names of all the operators which have ref in their entry in OpInfo (testing infra)
#   except for elementwise unary operators (separately implemented in test/test_unary_ufuncs.py),
#   elementwise binary operators (separately implemented in test_binary_ufuncs.py),
#   reduction operations (separately impelemented in test_reductions.py),
#   and Spectral Functions (separately implemented for only 1D as of now, in test/test_spectral_ops.py)
_ref_test_ops = tuple(
    filter(
        lambda op: not isinstance(
            op, (UnaryUfuncInfo, ReductionOpInfo, SpectralFuncInfo, BinaryUfuncInfo)
        )
        and op.ref is not None
        and op.ref is not _NOTHING,
        op_db,
    )
)
_ops_and_refs = op_db + python_ref_db

# Tests that apply to all operators and aren't related to any particular
#   system
class TestCommon(TestCase):
    exact_dtype = True

    # Verifies, on teardown, that no OpInfo is still using dynamic dtypes in CI
    @classmethod
    def tearDownClass(cls):
        super().tearDownClass()

        if IS_IN_CI:
            err_msg = (
                "The operator(s) below is(are) using dynamic_dtypes in the OpInfo entries."
                "This is OK for testing, but be sure to set the dtypes manually before landing your PR!"
            )
            # Assure no opinfo entry has dynamic_dtypes
            filtered_ops = list(filter(opinfo_helper.is_dynamic_dtype_set, op_db))
            for op in filtered_ops:
                fmt_str = opinfo_helper.str_format_dynamic_dtype(op)
                err_msg += "\n" + fmt_str

            assert len(filtered_ops) == 0, err_msg

    # Validates that each OpInfo specifies its forward and backward dtypes
    #   correctly for CPU and CUDA devices
    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @skipMeta
    @onlyNativeDeviceTypes
    @ops(ops_and_refs, dtypes=OpDTypes.none)
    def test_dtypes(self, device, op):
        # Check complex32 support only if the op claims.
        # TODO: Once the complex32 support is better, we should add check for complex32 unconditionally.
        device_type = torch.device(device).type
        include_complex32 = (
            (torch.complex32,)
            if op.supports_dtype(torch.complex32, device_type)
            else ()
        )

        # dtypes to try to backward in
        allowed_backward_dtypes = floating_and_complex_types_and(
            *((torch.half, torch.bfloat16) + include_complex32)
        )

        # lists for (un)supported dtypes
        supported_dtypes = set()
        unsupported_dtypes = set()
        supported_backward_dtypes = set()
        unsupported_backward_dtypes = set()

        def unsupported(dtype):
            unsupported_dtypes.add(dtype)
            if dtype in allowed_backward_dtypes:
                unsupported_backward_dtypes.add(dtype)

        for dtype in all_types_and_complex_and(
            *((torch.half, torch.bfloat16, torch.bool) + include_complex32)
        ):
            # tries to acquire samples - failure indicates lack of support
            requires_grad = dtype in allowed_backward_dtypes
            try:
                samples = tuple(
                    op.sample_inputs(device, dtype, requires_grad=requires_grad)
                )
            except Exception as e:
                unsupported(dtype)
                continue

            for sample in samples:
                # tries to call operator with the sample - failure indicates
                #   lack of support
                try:
                    result = op(sample.input, *sample.args, **sample.kwargs)
                    supported_dtypes.add(dtype)
                except Exception as e:
                    # NOTE: some ops will fail in forward if their inputs
                    #   require grad but they don't support computing the gradient
                    #   in that type! This is a bug in the op!
                    unsupported(dtype)
                    continue

                # Checks for backward support in the same dtype, if the input has
                # one or more tensors requiring grad
                def _tensor_requires_grad(x):
                    if isinstance(x, dict):
                        for k, v in x.items():
                            if _tensor_requires_grad(v):
                                return True
                    if isinstance(x, (list, tuple)):
                        for a in x:
                            if _tensor_requires_grad(a):
                                return True
                    if isinstance(x, torch.Tensor) and x.requires_grad:
                        return True

                    return False

                requires_grad = _tensor_requires_grad(sample.input) \
                    or _tensor_requires_grad(sample.args) or _tensor_requires_grad(sample.kwargs)
                if not requires_grad:
                    continue

                try:
                    result = sample.output_process_fn_grad(result)
                    if isinstance(result, torch.Tensor):
                        backward_tensor = result
                    elif isinstance(result, Sequence) and isinstance(
                        result[0], torch.Tensor
                    ):
                        backward_tensor = result[0]
                    else:
                        continue

                    # Note: this grad may not have the same dtype as dtype
                    # For functions like complex (float -> complex) or abs
                    #   (complex -> float) the grad tensor will have a
                    #   different dtype than the input.
                    #   For simplicity, this is still modeled as these ops
                    #   supporting grad in the input dtype.
                    grad = torch.randn_like(backward_tensor)
                    backward_tensor.backward(grad)
                    supported_backward_dtypes.add(dtype)
                except Exception as e:
                    unsupported_backward_dtypes.add(dtype)

        # Checks that dtypes are listed correctly and generates an informative
        #   error message

        supported_forward = supported_dtypes - unsupported_dtypes
        partially_supported_forward = supported_dtypes & unsupported_dtypes
        unsupported_forward = unsupported_dtypes - supported_dtypes
        supported_backward = supported_backward_dtypes - unsupported_backward_dtypes
        partially_supported_backward = (
            supported_backward_dtypes & unsupported_backward_dtypes
        )
        unsupported_backward = unsupported_backward_dtypes - supported_backward_dtypes

        device_type = torch.device(device).type

        claimed_forward = set(op.supported_dtypes(device_type))
        supported_but_unclaimed_forward = supported_forward - claimed_forward
        claimed_but_unsupported_forward = claimed_forward & unsupported_forward

        claimed_backward = set(op.supported_backward_dtypes(device_type))
        supported_but_unclaimed_backward = supported_backward - claimed_backward
        claimed_but_unsupported_backward = claimed_backward & unsupported_backward

        # Partially supporting a dtype is not an error, but we print a warning
        if (len(partially_supported_forward) + len(partially_supported_backward)) > 0:
            msg = "Some dtypes for {0} on device type {1} are only partially supported!\n".format(
                op.name, device_type
            )
            if len(partially_supported_forward) > 0:
                msg = (
                    msg
                    + "The following dtypes only worked on some samples during forward: {0}.\n".format(
                        partially_supported_forward
                    )
                )
            if len(partially_supported_backward) > 0:
                msg = (
                    msg
                    + "The following dtypes only worked on some samples during backward: {0}.\n".format(
                        partially_supported_backward
                    )
                )
            print(msg)

        if (
            len(supported_but_unclaimed_forward)
            + len(claimed_but_unsupported_forward)
            + len(supported_but_unclaimed_backward)
            + len(claimed_but_unsupported_backward)
        ) == 0:
            return

        # Reference operators often support additional dtypes, and that's OK
        if op in python_ref_db:
            if (
                len(claimed_but_unsupported_forward)
                + len(claimed_but_unsupported_backward)
            ) == 0:
                return

        # Generates error msg
        msg = "The supported dtypes for {0} on device type {1} are incorrect!\n".format(
            op.name, device_type
        )
        if len(supported_but_unclaimed_forward) > 0:
            msg = (
                msg
                + "The following dtypes worked in forward but are not listed by the OpInfo: {0}.\n".format(
                    supported_but_unclaimed_forward
                )
            )
        if len(supported_but_unclaimed_backward) > 0:
            msg = (
                msg
                + "The following dtypes worked in backward but are not listed by the OpInfo: {0}.\n".format(
                    supported_but_unclaimed_backward
                )
            )
        if len(claimed_but_unsupported_forward) > 0:
            msg = (
                msg
                + "The following dtypes did not work in forward but are listed by the OpInfo: {0}.\n".format(
                    claimed_but_unsupported_forward
                )
            )
        if len(claimed_but_unsupported_backward) > 0:
            msg = (
                msg
                + "The following dtypes did not work in backward but are listed by the OpInfo: {0}.\n".format(
                    claimed_but_unsupported_backward
                )
            )

        self.fail(msg)

    # Validates that each OpInfo works correctly on different CUDA devices
    @onlyCUDA
    @deviceCountAtLeast(2)
    @ops(op_db, allowed_dtypes=(torch.float32, torch.long))
    def test_multiple_devices(self, devices, dtype, op):
        for cuda_device_str in devices:
            cuda_device = torch.device(cuda_device_str)
            # NOTE: only tests on first sample
            samples = op.sample_inputs(cuda_device, dtype)
            sample = first_sample(self, samples)
            result = op(sample.input, *sample.args, **sample.kwargs)

            if isinstance(result, torch.Tensor):
                self.assertTrue(result.device == cuda_device)
            elif is_iterable_of_tensors(result):
                self.assertTrue(all(map(lambda t: t.device == cuda_device, result)))
            else:
                self.skipTest(
                    "Skipped! Only supports single tensor or iterable of tensor outputs."
                )

    # Tests that the function and its (ndarray-accepting) reference produce the same
    #   values on the tensors from sample_inputs func for the corresponding op.
    # This test runs in double and complex double precision because
    # NumPy does computation internally using double precision for many functions
    # resulting in possible equality check failures.
    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @onlyNativeDeviceTypes
    @suppress_warnings
    @ops(_ref_test_ops, allowed_dtypes=(torch.float64, torch.long, torch.complex128))
    def test_numpy_ref(self, device, dtype, op):
        try:
            # Sets the default dtype to NumPy's default dtype of double
            cur_default = torch.get_default_dtype()
            torch.set_default_dtype(torch.double)
            for sample_input in op.reference_inputs(device, dtype):
                self.compare_with_reference(
                    op, op.ref, sample_input, exact_dtype=(dtype is not torch.long)
                )
        finally:
            torch.set_default_dtype(cur_default)

    # Tests that experimental Python References can propagate shape, dtype,
    # and device metadata properly.
    # See https://github.com/pytorch/pytorch/issues/78050 for a discussion of stride propagation.
    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @onlyNativeDeviceTypes
    @ops(python_ref_db)
    def test_python_ref_meta(self, device, dtype, op):
        if dtype is torch.chalf:
            self.skipTest("Skipping chalf until it has more operator support")

        def _to_tensormeta(x):
            if isinstance(x, torch.Tensor):
                return prims.utils.TensorMeta(x)
            return x

        # TODO: iterate over requires_grad true/false
        inps = tuple(op.reference_inputs(device, dtype, requires_grad=False))
        for sample in op.reference_inputs(device, dtype, requires_grad=False):
            result = op(sample.input, *sample.args, **sample.kwargs)

            meta_sample = sample.transform(_to_tensormeta)
            meta_result = op(meta_sample.input, *meta_sample.args, **meta_sample.kwargs)

            if isinstance(result, torch.Tensor):
                prims.utils.compare_tensor_meta(result, meta_result)
            elif isinstance(result, Sequence):
                for a, b in zip(result, meta_result):
                    if isinstance(a, torch.Tensor) or isinstance(b, torch.Tensor):
                        prims.utils.compare_tensor_meta(a, b)

    def _ref_test_helper(self, ctx, device, dtype, op):
        if dtype is torch.chalf:
            self.skipTest("Skipping chalf until it has more operator support")

        # NOTE: this test works by comparing the reference
        ex = None
        for sample in op.reference_inputs(device, dtype, requires_grad=False):
            with ctx():
                ref_result = op(sample.input, *sample.args, **sample.kwargs)
            torch_result = op.torch_opinfo(sample.input, *sample.args, **sample.kwargs)

            for a, b in zip(tree_flatten(ref_result)[0], tree_flatten(torch_result)[0]):
                if isinstance(a, torch.Tensor) or isinstance(b, torch.Tensor):
                    prims.utils.compare_tensor_meta(a, b)
                    if getattr(op, 'validate_view_consistency', True):
                        self.assertEqual(a._is_view(), b._is_view())

            # Computes the dtype the more precise computatino would occur in
            precise_dtype = torch.bool
            if prims.utils.is_integer_dtype(dtype):
                # Note: bool and integer dtypes do not have more
                # precise dtypes -- they simply must be close
                precise_dtype = dtype
            if prims.utils.is_float_dtype(dtype):
                precise_dtype = torch.double
            if prims.utils.is_complex_dtype(dtype):
                precise_dtype = torch.cdouble

            # Checks if the results are close
            try:
                self.assertEqual(
                    ref_result,
                    torch_result,
                    exact_stride=False,
                    exact_device=True,
                    exact_layout=True,
                    exact_is_coalesced=True,
                )
            except AssertionError as e:
                # Raises the error if the precise dtype comparison wouldn't be
                # different
                if dtype is precise_dtype:
                    raise e

                ex = e


            # Goes to next sample if these results are close
            if not ex:
                continue

            # If the results are not close, checks that the
            # reference is more accurate than the torch op
            def _make_precise(x):
                if isinstance(x, torch.Tensor) and x.dtype is dtype:
                    return x.to(precise_dtype)
                return x

            precise_sample = sample.transform(_make_precise)
            precise_result = op.torch_opinfo(precise_sample.input, *precise_sample.args, **precise_sample.kwargs)

            def _distance(a, b):
                # Special-cases boolean comparisons
                if prims.utils.is_boolean_dtype(a.dtype):
                    assert b.dtype is torch.bool
                    return (a ^ b).sum()

                same = (a == b)
                if prims.utils.is_float_dtype(a.dtype) or prims.utils.is_complex_dtype(a.dtype):
                    same = torch.logical_or(same, torch.logical_and(torch.isnan(a), torch.isnan(b)))

                actual_error = torch.where(same, 0, torch.abs(a - b)).sum()
                return actual_error

            ref_distance = 0
            for a, b in zip(tree_flatten(ref_result)[0], tree_flatten(precise_result)[0]):
                ref_distance = ref_distance + _distance(a, b)

            torch_distance = 0
            for a, b in zip(tree_flatten(torch_result)[0], tree_flatten(precise_result)[0]):
                torch_distance = torch_distance + _distance(a, b)

            # TODO: consider adding some tolerance to this comparison
            msg = f"Reference result was farther ({ref_distance}) from the precise " \
                  "computation than the torch result was ({torch_distance})!"
            self.assertTrue(ref_distance <= torch_distance, msg=msg)

        # Reports numerical accuracy discrepancies
        if ex is not None:
            msg = "Test passed because the reference was more accurate than the torch operator."
            warnings.warn(msg)

    # Tests that experimental Python References perform the same computation
    # as the operators they reference, when operator calls in the torch
    # namesapce are remapped to the refs namespace (torch.foo becomes refs.foo).
    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @onlyNativeDeviceTypes
    @ops(python_ref_db)
    def test_python_ref(self, device, dtype, op):
        # In this test, primTorch refs call into the refs namespace
        # For example, a ref with torch.foo in it will calls refs.foo instead
        # Direct calls to refs and prims are not affected
        self._ref_test_helper(lambda: TorchRefsMode.push(strict=True), device, dtype, op)

    # Tests that experimental Python References perform the same computation
    # as the operators they reference, when operator calls in the torch
    # namespace are preserved (torch.foo remains torch.foo).
    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @onlyNativeDeviceTypes
    @ops(python_ref_db)
    def test_python_ref_torch_fallback(self, device, dtype, op):
        # In this test, refs call into the torch namespace (after the initial invocation)
        # For example, a ref with torch.foo in it will call torch.foo instead of refs.foo
        # Direct calls to refs and prims are not translated
        self._ref_test_helper(contextlib.nullcontext, device, dtype, op)

    @skipMeta
    @onlyNativeDeviceTypes
    @ops([op for op in op_db if op.error_inputs_func is not None], dtypes=OpDTypes.none)
    def test_errors(self, device, op):
        error_inputs = op.error_inputs(device)
        for ei in error_inputs:
            si = ei.sample_input
            with self.assertRaisesRegex(ei.error_type, ei.error_regex):
                op(si.input, *si.args, **si.kwargs)

    @skipMeta
    @onlyNativeDeviceTypes
    @ops([op for op in python_ref_db if op.error_inputs_func is not None], dtypes=OpDTypes.none)
    def test_python_ref_errors(self, device, op):
        def _to_tensormeta(x):
            if isinstance(x, torch.Tensor):
                return prims.utils.TensorMeta(x)
            return x

        error_inputs = op.error_inputs(device)
        for ei in error_inputs:
            si = ei.sample_input
            meta_sample = si.transform(_to_tensormeta)
            # TODO: match strings
            with self.assertRaisesRegex(ei.error_type, ""):
                op(meta_sample.input, *meta_sample.args, **meta_sample.kwargs)

    # Tests that the function produces the same result when called with
    #   noncontiguous tensors.
    # TODO: get working with Windows by addressing failing operators
    # TODO: get working with ASAN by addressing failing operators
    @unittest.skipIf(IS_WINDOWS, "Skipped under Windows")
    @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
    @onlyNativeDeviceTypes
    @suppress_warnings
    @ops(op_db, allowed_dtypes=(torch.float32, torch.long, torch.complex64))
    def test_noncontiguous_samples(self, device, dtype, op):
        test_grad = dtype in op.supported_backward_dtypes(torch.device(device).type)
        sample_inputs = op.sample_inputs(device, dtype, requires_grad=test_grad)
        for sample_input in sample_inputs:
            t_inp, t_args, t_kwargs = (
                sample_input.input,
                sample_input.args,
                sample_input.kwargs,
            )
            noncontig_sample = sample_input.noncontiguous()
            n_inp, n_args, n_kwargs = (
                noncontig_sample.input,
                noncontig_sample.args,
                noncontig_sample.kwargs,
            )

            # Verifies sample input tensors should have no grad or history
            sample_tensor = t_inp if isinstance(t_inp, torch.Tensor) else t_inp[0]
            assert sample_tensor.grad is None
            assert sample_tensor.grad_fn is None

            # validates forward
            expected = op(t_inp, *t_args, **t_kwargs)
            actual = op(n_inp, *n_args, **n_kwargs)

            self.assertEqual(actual, expected)

            # Validate backward
            # Short-circuits if the op doesn't support grad in this device x dtype
            if not test_grad:
                continue

            expected = sample_input.output_process_fn_grad(expected)
            actual = sample_input.output_process_fn_grad(actual)

            if isinstance(expected, torch.Tensor):
                grad_for_expected = torch.randn_like(expected)
                grad_for_actual = noncontiguous_like(grad_for_expected)
            elif isinstance(expected, Sequence):
                # Filter output elements that do not require grad
                expected = [
                    t
                    for t in expected
                    if isinstance(t, torch.Tensor) and t.requires_grad
                ]
                actual = [
                    n for n in actual if isinstance(n, torch.Tensor) and n.requires_grad
                ]
                grad_for_expected = [torch.randn_like(t) for t in expected]
                grad_for_actual = [noncontiguous_like(n) for n in grad_for_expected]
            else:
                # Nothing to do if it returns a scalar or things like that
                continue

            # Concatenate inputs into a tuple
            t_inputs = (
                (t_inp,) + t_args
                if isinstance(t_inp, torch.Tensor)
                else tuple(t_inp) + t_args
            )
            n_inputs = (
                (n_inp,) + n_args
                if isinstance(n_inp, torch.Tensor)
                else tuple(n_inp) + n_args
            )

            # Filter the elemnts that are tensors that require grad
            t_input_tensors = [
                t for t in t_inputs if isinstance(t, torch.Tensor) and t.requires_grad
            ]
            n_input_tensors = [
                n for n in n_inputs if isinstance(n, torch.Tensor) and n.requires_grad
            ]

            self.assertEqual(len(t_input_tensors), len(n_input_tensors))

            # Some functions may not use all the inputs to generate gradients. One of the
            # few examples of this "odd" behaviour is F.hinge_embedding_loss
            t_grads = torch.autograd.grad(
                expected, t_input_tensors, grad_for_expected, allow_unused=True
            )
            n_grads = torch.autograd.grad(
                actual, n_input_tensors, grad_for_actual, allow_unused=True
            )

            msg = "Got different gradients for contiguous / non-contiguous inputs wrt input {}."
            for i, (t, n) in enumerate(zip(t_grads, n_grads)):
                self.assertEqual(t, n, msg=msg.format(i))

    # Separates one case from the following test_out because many ops don't properly implement the
    #   incorrectly sized out parameter warning properly yet
    # Cases test here:
    #   - out= with the correct dtype and device, but the wrong shape
    @ops(_ops_and_refs, dtypes=OpDTypes.none)
    def test_out_warning(self, device, op):
        # Prefers running in float32 but has a fallback for the first listed supported dtype
        supported_dtypes = op.supported_dtypes(self.device_type)
        if len(supported_dtypes) == 0:
            self.skipTest("Skipped! Op has not supported dtypes on this device.")
        dtype = (
            torch.float32
            if torch.float32 in supported_dtypes
            else list(supported_dtypes)[0]
        )

        samples = op.sample_inputs(device, dtype)
        for sample in samples:
            # calls it normally to get the expected result
            expected = op(sample.input, *sample.args, **sample.kwargs)
            op_out = partial(op, sample.input, *sample.args, **sample.kwargs)

            # Short-circuits if output is not a single tensor or an
            #   iterable of tensors
            if not isinstance(expected, torch.Tensor) and not is_iterable_of_tensors(
                expected, include_empty=True
            ):
                self.skipTest(
                    "Skipped! Only supports single tensor or iterable of tensor outputs."
                )

            # Validates the op doesn't support out if it claims not to
            if not op.supports_out:
                with self.assertRaises(Exception):
                    assert op_out(out=expected) != NotImplemented
                return

            # A wrapper around map that works with single tensors and always
            #   instantiates the map. Used below to apply transforms to
            #   single tensor and iterable tensor outputs.
            def _apply_out_transform(fn, out):
                if isinstance(out, torch.Tensor):
                    return fn(out)

                # assumes (see above) that out is an iterable of tensors
                return tuple(map(fn, out))

            # Extracts strides from a tensor or iterable of tensors into a tuple
            def _extract_strides(out):
                if isinstance(out, torch.Tensor):
                    return (out.stride(),)

                # assumes (see above) that out is an iterable of tensors
                return tuple(map(lambda t: t.stride(), out))

            # Extracts data pointers from a tensor or iterable of tensors into a tuple
            # NOTE: only extracts on the CPU and CUDA device types since some
            #   device types don't have storage
            def _extract_data_ptrs(out):
                if self.device_type != "cpu" and self.device_type != "cuda":
                    return ()

                if isinstance(out, torch.Tensor):
                    return (out.data_ptr(),)

                # assumes (see above) that out is an iterable of tensors
                return tuple(map(lambda t: t.data_ptr(), out))

            @suppress_warnings
            def _compare_out(transform, *, compare_strides_and_data_ptrs=True):
                out = _apply_out_transform(transform, expected)
                original_strides = _extract_strides(out)
                original_ptrs = _extract_data_ptrs(out)

                op_out(out=out)
                final_strides = _extract_strides(out)
                final_ptrs = _extract_data_ptrs(out)

                self.assertEqual(expected, out)

                if compare_strides_and_data_ptrs:
                    stride_msg = "Strides are not the same! Original strides were {0} and strides are now {1}".format(
                        original_strides, final_strides
                    )
                    self.assertEqual(original_strides, final_strides, msg=stride_msg)
                    self.assertEqual(original_ptrs, final_ptrs)

            # Case Zero: out= with the correct dtype and device, but the wrong shape
            #   Expected behavior: if nonempty, resize with a warning.
            def _case_zero_transform(t):
                wrong_shape = list(t.shape)

                if len(wrong_shape) == 0:
                    # Handles scalar tensor case (empty list)
                    wrong_shape = [2]
                else:
                    wrong_shape[-1] = wrong_shape[-1] + 1
                return make_tensor(wrong_shape, dtype=t.dtype, device=t.device)

            # Verifies the out values are correct
            _compare_out(_case_zero_transform, compare_strides_and_data_ptrs=False)

            # Additionally validates that the appropriate warning is thrown if a nonempty
            #   tensor is resized.
            def _any_nonempty(out):
                if isinstance(out, torch.Tensor):
                    return out.numel() > 0

                return any(x.numel() > 0 for x in out)

            out = _apply_out_transform(_case_zero_transform, expected)
            msg_fail = "Resized a non-empty tensor but did not warn about it."
            if _any_nonempty(out):
                with self.assertWarnsRegex(
                    UserWarning, "An output with one or more elements", msg=msg_fail
                ):
                    op_out(out=out)

    # Validates ops implement the correct out= behavior
    # See https://github.com/pytorch/pytorch/wiki/Developer-FAQ#how-does-out-work-in-pytorch
    #   for a description of the correct behavior
    # Validates the following cases:
    #   - Case 0: out has the correct shape, dtype, and device but is full of extremal values
    #   - Case 1: out has the correct shape, dtype, and device but is noncontiguous
    #   - Case 2: out has the correct dtype and device, but is zero elements
    #   - Case 3: out has the correct shape and dtype, but is on a different device type
    #   - Case 4: out has the with correct shape and device, but a dtype that cannot
    #       "safely" cast to
    @ops(_ops_and_refs, dtypes=OpDTypes.any_one)
    def test_out(self, device, dtype, op):
        # Prefers running in float32 but has a fallback for the first listed supported dtype
        samples = op.sample_inputs(device, dtype)
        for sample in samples:
            # calls it normally to get the expected result
            expected = op(sample.input, *sample.args, **sample.kwargs)
            op_out = partial(op, sample.input, *sample.args, **sample.kwargs)

            # Short-circuits if output is not a single tensor or an
            #   iterable of tensors
            if not isinstance(expected, torch.Tensor) and not is_iterable_of_tensors(
                expected, include_empty=True
            ):
                self.skipTest(
                    "Skipped! Only supports single tensor or iterable of tensor outputs."
                )

            # Validates the op doesn't support out if it claims not to
            if not op.supports_out:
                with self.assertRaises(Exception):
                    assert op_out(out=expected) != NotImplemented
                return

            # A wrapper around map that works with single tensors and always
            #   instantiates the map. Used below to apply transforms to
            #   single tensor and iterable tensor outputs.
            def _apply_out_transform(fn, out):
                if isinstance(out, torch.Tensor):
                    return fn(out)

                # assumes (see above) that out is an iterable of tensors
                return tuple(map(fn, out))

            # Extracts strides from a tensor or iterable of tensors into a tuple
            def _extract_strides(out):
                if isinstance(out, torch.Tensor):
                    return (out.stride(),)

                # assumes (see above) that out is an iterable of tensors
                return tuple(map(lambda t: t.stride(), out))

            # Extracts data pointers from a tensor or iterable of tensors into a tuple
            # NOTE: only extracts on the CPU and CUDA device types since some
            #   device types don't have storage
            def _extract_data_ptrs(out):
                if self.device_type != "cpu" and self.device_type != "cuda":
                    return ()

                if isinstance(out, torch.Tensor):
                    return (out.data_ptr(),)

                # assumes (see above) that out is an iterable of tensors
                return tuple(map(lambda t: t.data_ptr(), out))

            def _compare_out(transform, *, compare_strides_and_data_ptrs=True):
                out = _apply_out_transform(transform, expected)
                original_strides = _extract_strides(out)
                original_ptrs = _extract_data_ptrs(out)

                op_out(out=out)
                final_strides = _extract_strides(out)
                final_ptrs = _extract_data_ptrs(out)
                self.assertEqual(expected, out)

                if compare_strides_and_data_ptrs:
                    stride_msg = "Strides are not the same! Original strides were {0} and strides are now {1}".format(
                        original_strides, final_strides
                    )
                    self.assertEqual(original_strides, final_strides, msg=stride_msg)
                    self.assertEqual(original_ptrs, final_ptrs)

            # Case 0: out= with the correct shape, dtype, and device
            #   but NaN values for floating point and complex tensors, and
            #   maximum values for integer tensors.
            #   Expected behavior: out= values have no effect on the computation.
            def _case_zero_transform(t):
                try:
                    info = torch.iinfo(t.dtype)
                    return torch.full_like(t, info.max)
                except TypeError as te:
                    # for non-integer types fills with NaN
                    return torch.full_like(t, float("nan"))


            _compare_out(_case_zero_transform)

            # Case 1: out= with the correct shape, dtype, and device,
            #   but noncontiguous.
            #   Expected behavior: strides are respected and `out` storage is not changed.
            def _case_one_transform(t):
                return make_tensor(
                    t.shape, dtype=t.dtype, device=t.device, noncontiguous=True
                )

            _compare_out(_case_one_transform)

            # Case 2: out= with the correct dtype and device, but has no elements.
            #   Expected behavior: resize without warning.
            def _case_two_transform(t):
                return make_tensor((0,), dtype=t.dtype, device=t.device)

            _compare_out(_case_two_transform, compare_strides_and_data_ptrs=False)

            # Also validates that no warning is thrown when this out is resized
            out = _apply_out_transform(_case_two_transform, expected)
            with warnings.catch_warnings(record=True) as caught:
                warnings.simplefilter("always")
                op_out(out=out)

            # Verifies no warning is a resize warning
            for w in caught:
                if "An output with one or more elements" in str(w.message):
                    self.fail(
                        "Resizing an out= argument with no elements threw a resize warning!"
                    )

            # Case 3: out= with correct shape and dtype, but wrong device.
            wrong_device = None
            if torch.device(device).type != "cpu":
                wrong_device = "cpu"
            elif torch.cuda.is_available():
                wrong_device = "cuda"

            if wrong_device is not None:

                def _case_three_transform(t):
                    return make_tensor(t.shape, dtype=t.dtype, device=wrong_device)

                out = _apply_out_transform(_case_three_transform, expected)
                msg_fail = f"Expected RuntimeError when calling with input.device={device} and out.device={wrong_device}"
                with self.assertRaises(RuntimeError, msg=msg_fail):
                    op_out(out=out)

            # Case 4: out= with correct shape and device, but a dtype
            #   that output cannot be "safely" cast to (long).
            #   Expected behavior: error.
            # NOTE: this case is filtered by dtype since some ops produce
            #   bool tensors, for example, which can be safely cast to any
            #   dtype. It is applied when single tensors are floating point or complex
            #   dtypes, or if an op returns multiple tensors when at least one such
            #   tensor is a floating point or complex dtype.
            _dtypes = floating_and_complex_types_and(torch.float16, torch.bfloat16)
            if (
                isinstance(expected, torch.Tensor)
                and expected.dtype in _dtypes
                or (
                    not isinstance(expected, torch.Tensor)
                    and any(t.dtype in _dtypes for t in expected)
                )
            ):

                def _case_four_transform(t):
                    return make_tensor(t.shape, dtype=torch.long, device=t.device)

                out = _apply_out_transform(_case_four_transform, expected)
                msg_fail = "Expected RuntimeError when doing an unsafe cast!"
                msg_fail = (
                    msg_fail
                    if not isinstance(expected, torch.Tensor)
                    else (
                        "Expected RuntimeError when doing an unsafe cast from a result of dtype "
                        f"{expected.dtype} into an out= with dtype torch.long"
                    )
                )
                with self.assertRaises(RuntimeError, msg=msg_fail):
                    op_out(out=out)

    # Tests that the forward and backward passes of operations produce the
    #   same values for the cross-product of op variants (method, inplace)
    #   against eager's gold standard op function variant
    @_variant_ops(op_db)
    def test_variant_consistency_eager(self, device, dtype, op):
        # Acquires variants (method variant, inplace variant, operator variant, inplace_operator variant, aliases)

        method = op.method_variant
        inplace = op.inplace_variant
        operator = op.operator_variant
        inplace_operator = op.inplace_operator_variant


        # list of all inplace ops: inplace variant + alias inplace variants if exist
        inplace_ops = [inplace, inplace_operator]
        variants = [method, inplace, operator, inplace_operator]
        operators = [operator, inplace_operator]

        for a_op in op.aliases:
            variants.append(a_op.op)
            variants.append(a_op.method_variant)
            variants.append(a_op.inplace_variant)
            inplace_ops.append(a_op.inplace_variant)

        inplace_variants = tuple(filter(None, inplace_ops))
        variants = tuple(filter(None, variants))
        operators = tuple(filter(None, operators))

        _requires_grad = dtype in op.supported_backward_dtypes(
            torch.device(device).type
        )

        include_conjugated_inputs = op.test_conjugated_samples and dtype.is_complex
        samples = op.sample_inputs(
            device,
            dtype,
            requires_grad=_requires_grad,
            include_conjugated_inputs=include_conjugated_inputs,
        )
        samples = list(samples)

        def _test_consistency_helper(samples, variants):
            for sample in samples:
                # TODO: Check grad for all Tensors requiring grad if sample.input is TensorList
                tensor = (
                    sample.input
                    if isinstance(sample.input, torch.Tensor)
                    else sample.input[0]
                )

                # Computes function forward and backward values
                tensor.grad = None
                expected_forward = op(sample.input, *sample.args, **sample.kwargs)
                expected_grad = None

                output_process_fn_grad = (
                    sample.output_process_fn_grad
                    if sample.output_process_fn_grad
                    else lambda x: x
                )

                # Skips inplace variants if the output dtype is not the same as
                #   the input dtype
                skip_inplace = False
                if (
                    isinstance(expected_forward, torch.Tensor)
                    and expected_forward.dtype is not tensor.dtype
                ):
                    skip_inplace = True

                # TODO: backward consistency only supported for single tensor outputs
                # TODO: backward consistency only checked on sample.input, not all
                #   tensor inputs
                # TODO: update to handle checking grads of all tensor inputs as
                #   derived from each tensor output
                if isinstance(
                    expected_forward, torch.Tensor
                ) and dtype in op.supported_backward_dtypes(torch.device(device).type):
                    output_process_fn_grad(expected_forward).sum().backward()
                    expected_grad = tensor.grad

                # Test eager consistency
                for variant in variants:
                    # Skips inplace ops
                    if variant in inplace_ops and skip_inplace:
                        continue

                    # Compares variant's forward
                    # Note: copies the to-be-modified input when testing the inplace variant
                    tensor.grad = None
                    cloned = (
                        clone_input_helper(sample.input)
                        if variant in inplace_ops
                        else sample.input
                    )

                    if variant in inplace_ops and sample.broadcasts_input:
                        with self.assertRaises(
                            RuntimeError,
                            msg=(
                                "inplace variant either incorrectly allowed "
                                "resizing or you have marked the sample {}"
                                " incorrectly with `broadcasts_self=True".format(
                                    sample.summary()
                                )
                            ),
                        ):
                            variant_forward = variant(
                                cloned, *sample.args, **sample.kwargs
                            )
                        continue

                    if variant in operators and sample.kwargs:
                        # skip samples with kwargs for operator variants
                        continue

                    variant_forward = variant(cloned, *sample.args, **sample.kwargs)
                    self.assertEqual(expected_forward, variant_forward)

                    # Compares variant's backward
                    if expected_grad is not None and (
                        variant not in inplace_ops or op.supports_inplace_autograd
                    ):
                        output_process_fn_grad(variant_forward).sum().backward()
                        self.assertEqual(expected_grad, tensor.grad)

        _test_consistency_helper(samples, variants)

        def _test_inplace_preserve_storage(samples, variants):
            for sample in samples:
                # Skips inplace variants if the output dtype is not the same as
                #   the input dtype
                expected_forward = op(sample.input, *sample.args, **sample.kwargs)
                tensor = (
                    sample.input
                    if isinstance(sample.input, torch.Tensor)
                    else sample.input[0]
                )
                skip_inplace = False
                if (
                    isinstance(expected_forward, torch.Tensor)
                    and expected_forward.dtype is not tensor.dtype
                ):
                    skip_inplace = True
                if skip_inplace:
                    return
                for variant in variants:
                    cloned = (
                        clone_input_helper(sample.input)
                        if variant in inplace_ops
                        else sample.input
                    )
                    inp_tensor = (
                        cloned if isinstance(cloned, torch.Tensor) else cloned[0]
                    )
                    data_ptr = inp_tensor.data_ptr()
                    if variant in operators and sample.kwargs:
                        # skip samples with kwargs for operator variants
                        continue

                    variant_forward = variant(cloned, *sample.args, **sample.kwargs)
                    # TODO Support non-tensor outputs if they exist for inplace ops
                    if isinstance(variant_forward, torch.Tensor):
                        self.assertEqual(
                            data_ptr, variant_forward.data_ptr(), atol=0, rtol=0
                        )
                    else:
                        self.assertTrue(
                            False,
                            "Non-tensor outputs for inplace ops are not supported",
                        )

        if len(inplace_ops) > 0:
            inplace_samples = list(
                filter(lambda sample: not sample.broadcasts_input, samples)
            )
            _test_inplace_preserve_storage(inplace_samples, inplace_variants)

    # Reference testing for operations in complex32 against complex64.
    # NOTE: We test against complex64 as NumPy doesn't have a complex32 equivalent dtype.
    @ops(op_db, allowed_dtypes=(torch.complex32,))
    def test_complex_half_reference_testing(self, device, dtype, op):
        if not op.supports_dtype(torch.complex32, device):
            unittest.skip("Does not support complex32")

        for sample in op.sample_inputs(device, dtype):
            actual = op(sample.input, *sample.args, **sample.kwargs)
            # sample.transform applies the lambda to torch.Tensor and torch.dtype.
            # However, we only want to apply it to Tensors with dtype `torch.complex32`..
            transformed_sample = sample.transform(lambda x: x.to(torch.complex64) if isinstance(
                x, torch.Tensor) and x.dtype is torch.complex32 else x)
            expected = op(
                transformed_sample.input,
                *transformed_sample.args,
                **transformed_sample.kwargs,
            )
            # Since range of chalf is much less compared to cfloat,
            # we get `inf`s easily (eg. with `pow`, `exp`),
            # so we cast `cfloat` back to `chalf`.
            expected = tree_map(lambda x: x.to(torch.complex32) if isinstance(
                x, torch.Tensor) and x.dtype is torch.complex64 else x, expected)

            # `exact_dtype` is False because for ops like real, imag
            # we get different dtypes for `actual` and `expected`
            # `chalf` input -> `half` output
            # `cfloat` input -> `float` output
            self.assertEqual(actual, expected, exact_dtype=False)


class TestCompositeCompliance(TestCase):
    # Checks if the operator (if it is composite) is written to support most
    # backends and Tensor subclasses. See "CompositeImplicitAutograd Compliance"
    # in aten/src/ATen/native/README.md for more details
    @unittest.skipIf(
        IS_FBCODE or IS_SANDCASTLE, "__torch_dispatch__ does not work in fbcode"
    )
    @ops(op_db, allowed_dtypes=(torch.float,))
    def test_operator(self, device, dtype, op):
        samples = op.sample_inputs(device, dtype, requires_grad=False)

        for sample in samples:
            args = [sample.input] + list(sample.args)
            kwargs = sample.kwargs
            composite_compliance.check_with_mode(op, args, kwargs)
            composite_compliance.check_all_permutations(op, args, kwargs)

    @unittest.skipIf(
        IS_FBCODE or IS_SANDCASTLE, "__torch_dispatch__ does not work in fbcode"
    )
    @ops([op for op in op_db if op.supports_autograd], allowed_dtypes=(torch.float,))
    def test_backward(self, device, dtype, op):
        samples = op.sample_inputs(device, dtype, requires_grad=True)

        for sample in samples:
            args = [sample.input] + list(sample.args)
            kwargs = sample.kwargs
            composite_compliance.check_backward_formula(op, args, kwargs)

    @unittest.skipIf(
        IS_FBCODE or IS_SANDCASTLE, "__torch_dispatch__ does not work in fbcode"
    )
    @ops(op_db, allowed_dtypes=(torch.float,))
    def test_forward_ad(self, device, dtype, op):
        if torch.float not in op.supported_backward_dtypes(device):
            raise unittest.SkipTest("Does not support autograd")

        if not op.supports_forward_ad:
            raise unittest.SkipTest("Does not support forward_ad")

        samples = op.sample_inputs(device, dtype, requires_grad=True)

        for sample in samples:
            args = [sample.input] + list(sample.args)
            kwargs = sample.kwargs
            composite_compliance.check_forward_ad_formula(op, args, kwargs)


class TestMathBits(TestCase):
    # Tests that
    # 1. The operator's output for physically conjugated/negated tensors and conjugate/negative view tensors
    # produces the same value
    # 2. The gradients are same in both cases mentioned in (1)
    # 3. If the operator's inplace variant is supported, tests that the inplace operation
    #    produces the correct value when called on a conjugate/negative view tensor and that the output
    #    has its conj/neg bit set to true
    # This test only runs for C -> R and C -> C functions
    # TODO: add tests for `R->C` functions
    # Note: This test runs for functions that take both tensors and tensorlists as input.
    def _test_math_view(
        self,
        device,
        dtype,
        op,
        samples,
        math_op_physical,
        math_op_view,
        is_bit_set,
        out_type,
    ):
        inplace_variant = op.inplace_variant

        # helper function to clone and conjugate/negate the input if its a tensor
        # else clone the sequence and conjugate/negate the first element in the sequence
        # If a requires_grad argument is provided the tensor being conjugated/negated will
        # have its requires_grad set to that value.
        def clone_and_perform_view(input, **kwargs):
            if isinstance(input, torch.Tensor):
                requires_grad = kwargs.get("requires_grad", input.requires_grad)
                with torch.no_grad():
                    # Ensure view represents the original sample input
                    input = math_op_physical(input)
                # Note: .conj() is not called under no_grad mode since it's not allowed to modify a
                # view created in no_grad mode. Here it's ok to do so, so as a workaround we call conj
                # before resetting the requires_grad field for input
                input = math_op_view(input)
                assert input.is_leaf
                return input.requires_grad_(requires_grad)

            if isinstance(input, Sequence):
                out = list(map(clone_input_helper, input))
                out[0] = clone_and_perform_view(out[0])
                return tuple(out)

        for sample in samples:
            tensor = (
                sample.input
                if isinstance(sample.input, torch.Tensor)
                else sample.input[0]
            )
            cloned1 = clone_and_perform_view(sample.input)

            # Computes function forward value with a physically conjugated/negated tensor and
            # a conj/neg view tensor and verifies that the output in both case are equal.
            expected_forward = op(sample.input, *sample.args, **sample.kwargs)
            forward_with_mathview = op(cloned1, *sample.args, **sample.kwargs)
            self.assertEqual(expected_forward, forward_with_mathview)

            # If the op has an inplace variant, and the input doesn't require broadcasting
            # and has the same dtype as output, verify that the inplace operation on a conjugated/negated
            # input produces correct output, and the output tensor has the conj/neg bit set to True
            if inplace_variant is not None and not sample.broadcasts_input:
                cloned2 = clone_and_perform_view(tensor, requires_grad=False)
                if (
                    isinstance(expected_forward, torch.Tensor)
                    and expected_forward.dtype is tensor.dtype
                ):
                    inplace_forward = inplace_variant(
                        cloned2, *sample.args, **sample.kwargs
                    )
                    self.assertTrue(is_bit_set(inplace_forward))
                    self.assertEqual(inplace_forward, expected_forward)

            # TODO: backward consistency only supported for single tensor outputs
            # TODO: backward consistency only checked on sample.input, not all
            #   tensor inputs
            # TODO: update to handle checking grads of all tensor inputs as
            #   derived from each tensor output
            if (
                isinstance(expected_forward, torch.Tensor)
                and expected_forward.requires_grad
            ):
                output_process_fn_grad = sample.output_process_fn_grad or (lambda x: x)
                expected_forward = output_process_fn_grad(expected_forward)
                forward_with_mathview = output_process_fn_grad(forward_with_mathview)

                tensor = (
                    sample.input
                    if isinstance(sample.input, torch.Tensor)
                    else sample.input[0]
                )
                expected_forward.sum().backward(retain_graph=True)
                forward_with_mathview.sum().backward(retain_graph=True)
                if tensor.grad is not None:
                    cloned1_tensor = (
                        cloned1 if isinstance(cloned1, torch.Tensor) else cloned1[0]
                    )
                    self.assertEqual(tensor.grad, cloned1_tensor.grad)

                    tensor.grad, cloned1_tensor.grad = None, None

                    # a repeat of the above test if output is not complex valued
                    if out_type(expected_forward):
                        grad = torch.randn_like(expected_forward)
                        expected_forward.backward(grad)
                        forward_with_mathview.backward(
                            math_op_view(math_op_physical(grad))
                        )

                        self.assertEqual(tensor.grad, cloned1_tensor.grad)

    @ops(ops_and_refs, allowed_dtypes=(torch.cfloat,))
    def test_conj_view(self, device, dtype, op):
        if not op.test_conjugated_samples:
            self.skipTest("Operation doesn't support conjugated inputs.")
        math_op_physical = torch.conj_physical
        math_op_view = torch.conj
        _requires_grad = torch.cfloat in op.supported_backward_dtypes(
            torch.device(device).type
        )
        is_bit_set = torch.is_conj
        samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad)
        self._test_math_view(
            device,
            dtype,
            op,
            samples,
            math_op_physical,
            math_op_view,
            is_bit_set,
            torch.is_complex,
        )

    @ops(ops_and_refs, allowed_dtypes=(torch.double,))
    def test_neg_view(self, device, dtype, op):
        if not op.test_neg_view:
            self.skipTest("Operation not tested with tensors with negative bit.")
        math_op_physical = torch.neg
        math_op_view = torch._neg_view
        is_bit_set = torch.is_neg
        samples = op.sample_inputs(device, dtype, requires_grad=op.supports_autograd)
        self._test_math_view(
            device,
            dtype,
            op,
            samples,
            math_op_physical,
            math_op_view,
            is_bit_set,
            lambda x: True,
        )

    @ops(ops_and_refs, allowed_dtypes=(torch.cdouble,))
    def test_neg_conj_view(self, device, dtype, op):
        if not op.test_neg_view:
            self.skipTest("Operation not tested with tensors with negative bit.")
        if not op.test_conjugated_samples:
            self.skipTest("Operation doesn't support conjugated inputs.")

        def math_op_physical(x):
            return -x.conj_physical()

        def math_op_view(x):
            return torch._neg_view(x).conj()

        def is_bit_set(x):
            return torch.is_neg(x) and torch.is_conj(x)

        _requires_grad = dtype in op.supported_backward_dtypes(
            torch.device(device).type
        )
        samples = op.sample_inputs(device, dtype, requires_grad=_requires_grad)
        # Only test one sample
        samples = itertools.islice(samples, 1)
        self._test_math_view(
            device,
            dtype,
            op,
            samples,
            math_op_physical,
            math_op_view,
            is_bit_set,
            torch.is_complex,
        )



class TestRefsOpsInfo(TestCase):

    import_paths = ["_refs", "_refs.special", "_refs.nn.functional"]
    module_alls = [(path, import_module(f"torch.{path}").__all__) for path in import_paths]
    ref_ops_names = itertools.chain.from_iterable(
        [f"{path}.{op}" for op in module_all] for path, module_all in module_alls)
    ref_db_names = set(ref_op.name for ref_op in python_ref_db)

    # TODO: References that do not have an entry in python_ref_db
    skip_ref_ops = {
        '_refs.bitwise_right_shift',
        '_refs.copy_to',
        '_refs.empty_strided',
        '_refs.equal',
        '_refs.full',
        '_refs.full_like',
        '_refs.item',
        '_refs.ones',
        '_refs.ones_like',
        '_refs.std_var',
        '_refs.swap_axes',
        '_refs.uniform',
        '_refs.zeros',
        '_refs.zeros_like'
    }

    @parametrize("op", ref_ops_names)
    def test_refs_are_in_python_ref_db(self, op):
        if op in self.skip_ref_ops:
            raise unittest.SkipTest(f"{op} does not have an entry in python_ref_db")
        self.assertIn(op, self.ref_db_names)


instantiate_device_type_tests(TestCommon, globals())
instantiate_device_type_tests(TestCompositeCompliance, globals())
instantiate_device_type_tests(TestMathBits, globals())
instantiate_device_type_tests(TestRefsOpsInfo, globals(), only_for="cpu")

if __name__ == "__main__":
    run_tests()